An unlabelled peptide expression data file called “pep_edata.csv” was uploaded to pmart. The column that designates unique molecules was marked as “Mass_Tag_ID”. The original scale of the data was abundance and was changed to log2. The value to denote missing data was “NA” and the expression data was not already normalized.
An associated biomolecule information file was also uploaded called “pep_emeta.csv” and the protein identifier column was designated as “Protein”.
An associated sample information file was also uploaded called “pep_fdata.csv”. Trimmed sample names were not used. The column in the sample information file which indicates sample names was designated as “SampleID”.
This table summarizes all the user specified main effects or covariates in pmart. The “Selected Column Name” denotes the name of the column in the sample information file assigned as a main effect or covariate.
| Main Effect or Covariate | Selected Column Name |
|---|---|
| First Main Effect | Condition |
| Second Main Effect | None selected |
| First Covariate | None selected |
| Second Covariate | None selected |
The first column in the table below denotes a property of the peptide data, and the “Data” column states that property’s value.
| Data | |
|---|---|
| Class | pepData |
| Unique SampleIDs (f_data) | 10 |
| Unique Mass_Tag_IDs (e_data) | 13022 |
| Unique Proteins (e_meta) | NA |
| Missing Observations | 35317 |
| Proportion Missing | 0.271 |
| Samples per group: Infection | 7 |
| Samples per group: Mock | 3 |
In the table below, the first column denotes the sample and the second is the missing number of observations. The third column represents the second as a percentage of the total number of observations for that sample.
| Missing Observations | Proportion Missing | |
|---|---|---|
| Infection2 | 2711 | 0.208 |
| Infection3 | 3447 | 0.265 |
| Infection4 | 3978 | 0.305 |
| Infection6 | 3535 | 0.271 |
| Infection7 | 2896 | 0.222 |
| Infection8 | 6014 | 0.462 |
| Infection9 | 4953 | 0.380 |
| Mock1 | 2682 | 0.206 |
| Mock2 | 2551 | 0.196 |
| Mock3 | 2550 | 0.196 |
Filters were applied. A total of 6 filters were applied. See the table below for a descriptions of the filters and the order.
| Order | Filter | Type | Parameters | Summary |
|---|---|---|---|---|
| 1 | Molecule Filter | Biomolecule | Min Number Molecules: 2 | 1814 biomolecule(s) were filtered. |
| 2 | Proteomics Filter | Biomolecule | Min Number of Peptides: 2 & Degenerate Peptides Removed: Yes | 708 biomolecule(s) were filtered and 952 protein(s) were filtered. |
| 3 | CV Filter | Biomolecule | Max CV: 150 | 2 biomolecule(s) were filtered. |
| 4 | imd-ANOVA Filter | Biomolecule | Min ANOVA: 2 & Min G-Test: 3 | 3866 biomolecule(s) were filtered. |
| 5 | rMD Filter | Sample | P-Value Threshold: 0.001 & Metrics Used: MAD, Kurtosis, Skewness, Correlation | 1 sample(s) were filtered. |
| 6 | Custom Filter | Sample or Biomolecule | 1 sample(s) were filtered. 0 biomolecule(s) were filtered. 0 protein(s) were filtered. |
A molecule filter was applied to the data, which removes biomolecule(s) (Mass_Tag_IDs) not having at least the minimum number of samples (Min Number Molecules).
A coefficient of variation (CV) filter was applied to the data which removes biomolecule(s) (Mass_Tag_IDs) with a CV greater than the threshold (Max CV).
An ANOVA filter can be applied to the data which removes biomolecule(s) (Mass_Tag_IDs) not having at least a minimum number of non-missing values per group (Min ANOVA). Additionaly, an IMD (independence of missing data) filter can be applied to the data, removing biomolecules not having at least a certain number of non-missing values (Min G-Test) in at least one of the groups.
A degenerate peptide filter (Degenerate Peptides Removed) can be applied to the data, which identifies biomolecule(s) (Mass_Tag_IDs) not belonging to one protein. Additionally, a protein filter can be applied to the data which identifies proteins not having at least a minimum number of peptides (Min Number of Peptides) mapping to them.
A robust Mahalanobis distance (rMD) filter was applied to the data, removing sample(s) (SampleIDs) with an associated rMD-associated p-value less than the threshold (P-Value Threshold). Metrics used to calculate the p-value are also included (Metrics Used).
Custom filters can be used to remove samples, biomolecules, or proteins.
Peptide data was normalized.
SPANS was run to determine optimum normalization parameters.
Manual normalization was used to normalize the data.
| Attribute | Value |
|---|---|
| Subset Function | all |
| Subset Parameters | |
| Normalization Function | mean |
The analysis step was run.
The following groups were compared: Infection_vs_Mock. Reported below are the parameters used in the statistical analysis, followed by the number of siginificant biomolecules for each comparison.
| Attribute | Value |
|---|---|
| Test Method | anova |
| Multiple Comparison Adjustment | holm |
| Significance Threshold | 0.05 |
| Comparison | Up_total | Down_total | Up_anova | Down_anova | Up_gtest | Down_gtest | |
|---|---|---|---|---|---|---|---|
| Infection_vs_Mock | Infection_vs_Mock | 1509 | 2742 | 1509 | 2742 | 0 | 0 |